Shadow removal algorithm with shadow area border processing

Shedlovska, Y. I.; Hnatushenko, Volodymyr

doi:10.1109/ysf.2016.7753827

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…Till date researchers have proposed various feature-based methods, which mainly have the basis of shadow detection index or features obtained from near infrared channels or multispectral view etc [11]. Y. I. Shedlovska et.al (2016) [12], published their research paper which provides a simple and enhanced technique to detect as well as remove shadow in satellite images which can be useful for the same operation on Soybean seed sample images. In this method, the input image is transformed from RGB (Red, Green, Blue) to HSV (Hue, Saturation, Value) color scheme; the normalized saturation-value difference index is calculated to determine the shadowed region in an image as it can indicate higher values for the image components in shadowed region as compared to image components in nonshadowed regions.…”

Section: Shadow Removalmentioning

confidence: 99%

A Literature Review on Image Processing and Classification Techniques for Agriculture Produce and Modeling of Quality Assessment system for Soybean industry Sample

2019

International Journal of Innovative Research in Electronics and

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Soybean, the most popular golden bean of America, is widely known for its fat free food products. Richness in Protein makes it one of the best suggested meals which can be consumed in the form of pulses, oil, food for animals etc. The quality of such products is mainly dependent on the quality of Soybean procured as fresh farm produce. Governments and regional authorities have already defined the standards for quality assessment and grading of Soybean, which are meant to be followed in the commercial market while trading. Presently, visual inspection is the preferred way to conduct physical quality assessment of Soybean and it is performed by an expert person, at the time of procurement of Soybean based on the standards recommended by the buying authority. Physical parameters of Soybean kernel like color, growth corresponding to size, damage, fungi/ disease as well as mixing of other material/ objects in soybean sample influence the grade of that sample during quality assessment. Dependency of this process on human expert usually harms the accuracy in assessment. Therefore, an automated machine is desired to be a suitable solution to address this issue and can benefit in terms of increased accuracy, reliability, and reduced response time. Worldwide researchers are working on designing such automated systems for different type of fruits, grains etc. However, in case of Soybean, up till now we are successful in cleaning, sorting, color detection, and also, through various image processing techniques and algorithms researchers could detect the anomalies present in grain sample. But an automated system for the quality assessment and grading of Soybean according to an International Standard is yet to be implemented. In this paper, we propose a two-stage model for the quality assessment of Soybean; first stage focuses on image processing techniques like Image acquisition, preprocessing and feature extraction Likewise the second stage works on classification of Soybean kernels and sample grading with the help of a machine learning technique on the basis of an International Standard.

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Section: Shadow Removalmentioning

confidence: 99%

A Literature Review on Image Processing and Classification Techniques for Agriculture Produce and Modeling of Quality Assessment system for Soybean industry Sample

2019

International Journal of Innovative Research in Electronics and

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“…1). Currently, hillshade extraction methods employ either graphics methods or modeling approaches [5]- [7]. Graphic methods usually use remote sensing images to identify shadows through processes such as thresholding [8], edge detection [7], and region growth [9].…”

Section: Introductionmentioning

confidence: 99%

To Ignore or Not: Understanding the Influence of Hillshade

Xian,

Wang,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hillshade poses an important challenge in remote sensing research at mountainous regions. In order to understand the influence of hillshade on the solar direct radiation (SDR) transfer process, this study performed the calculation of hillshade (including both self-shadow and cast-shadow) and the evaluation of its radiative effects over the Tibetan Plateau. When utilizing the ray tracing method to calculate hillshade, a search radius of 6 km (equivalent to 200 NASADEM pixels) efficiently captures about 99% of the hillshade area at a solar zenith angle (SZA) of 80°. And the necessary search radius decreases rapidly as the SZA decreases. By calculating the hillshade on the Tibetan Plateau, it was found that the surface experiences hillshade for approximately 10% of daylight hours, with more than 30% in the most severely impacted areas. The hillshade dynamics over the Tibetan Plateau reveal minimal seasonal variation but significant diurnal differences, moreover, the impact of hillshade varies throughout the day. It is worth noting that one hour after sunrise and one hour before sunset, the impact of hillshade reaches its maximum, with over 30% of the area covered by hillshade. In particular, ignoring the castshadow can lead to overestimating the average SDR of the entire plateau by 40 W/m² . This impact is most severe during the winter solstice. In contrast, with a smaller SZA, the influence of hillshade on SDR is minimal at noon.

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A Very High Resolution Satellite Imagery Classification Algorithm

Shedlovska

Hnatushenko

2018

2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO)

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Shadow removal algorithm with shadow area border processing

Cited by 7 publications

References 13 publications

A Literature Review on Image Processing and Classification Techniques for Agriculture Produce and Modeling of Quality Assessment system for Soybean industry Sample

A Literature Review on Image Processing and Classification Techniques for Agriculture Produce and Modeling of Quality Assessment system for Soybean industry Sample

To Ignore or Not: Understanding the Influence of Hillshade

A Very High Resolution Satellite Imagery Classification Algorithm

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